Towards SLO-aware Resource Scheduling for Serverless Inference Workloads

The rapid advancement of Machine Learning (ML) and Deep Learning (DL) has revolutionized various domains, necessitating efficient and cost-effective ML inference capabilities. Function-as-a-Service (FaaS) has emerged as a promising approach for hosting ML inference services, providing a serverless computing environment that streamlines development cycles and offers scalability and simplified infrastructure management. However, existing autoscaling strategies employed by popular FaaS platforms often overlook critical factors such as response time and tail latency. Additionally, Python's Global Interpreter Lock (GIL) poses challenges for parallel computing in high-request traffic scenarios. This thesis addresses the need for efficient and cost-effective Machine Learning (ML) inference capabilities by exploring batching and autoscaling strategies for Serverless Inference instances. The study proposes a prototype FaaS framework that provides adaptive request batching, reactive autoscaling policies, and SLO monitoring, thus allowing Serverless Inference workloads to meet their SLO targets even during peak traffic. The proposed approach aims to optimize resource utilization, mitigate tail latency, and improve overall system performance.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science and Applications

Department dc:contributor.department

Computer Science and Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2023

Author dc:creator

• Tripathy, Abhijit

Chair dc:contributor.committeechair

• Butt, Ali R.

Committee members dc:contributor.committeemember

• Rafique, Muhammad Mustafa
• Nikolopoulos, Dimitrios S.

• Machine Learning
• Deep Learning
• Serverless Inference
• Autoscaling
• Load Balancing
• Response Time
• Tail Latency

Statement dc:rights

• Creative Commons Attribution 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:38236

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/116005